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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
06/10/2011 |
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Data da última atualização: |
07/10/2014 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MARQUES, E. M. G.; FERRÃO, R. G.; FERRAO, M. A. G.; FONSECA, A. F. A. da; BRAGANÇA, S. M.; ARLEU, R. J. |
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Afiliação: |
EMCAPER; EMCAPER; MARIA AMELIA GAVA FERRAO, SAPC; AYMBIRE FRANCISCO A DA FONSECA, SAPC; EMCAPER; EMCAPER. |
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Título: |
Competição de variedades de café conilon (Coffea canephora) para Região Sul do Estado do Espírito Santo. |
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Ano de publicação: |
2000 |
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Fonte/Imprenta: |
IN: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 1., 2000, Poços de Caldas. Resumos Expandidos. Brasília, DF: Embrapa Café/MINASPLAN, 2000. |
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Idioma: |
Português |
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Conteúdo: |
Os trabalhos de melhoramento de café Conilon da EMCAPER, objetivando disponibilizar variedades melhoradas para os produtores capixabas, abrangeram inicialmente o Norte do Estado do Espírito Santo. Considerando que a área de plantio vem sendo ampliada no Sul, os experimentos de competição passaram a ser conduzidos também nessa região. O Ensaio Regional de Café Conilon foi instalado em maio de 1997, na Fazenda Experimental de Bananal do Norte - FEBN, da EMCAPER, no município de Cachoeiro de Itapemirim, em blocos casualizados, 4 repetições e 8 tratamentos, constituídos de uma variedade de semente, três variedades clonais lançadas pela Ex-EMCAPA, três variedades clonais experimentais e, um Clone Elite, como testemunha. O rendimento médio do experimento variou entre 86 a 118 sc/ben/ha, correspondendo a variedade de semente (EMCAPA 8150) e ao Clone Elite (testemunha), respectivamente. A variedade experimental EMCAPA 8122, de ciclo intermediário, mostrou-se promissora para a região, com rendimento médio de 112 sc/ben/ha. |
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Palavras-Chave: |
Cultivar. |
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Thesagro: |
Clone; Melhoramento Vegetal; Seleção. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/42974/1/Competicao-de-variedades-de-cafe.pdf
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Marc: |
LEADER 01800nam a2200217 a 4500
001 1902635
005 2014-10-07
008 2000 bl uuuu u00u1 u #d
100 1 $aMARQUES, E. M. G.
245 $aCompetição de variedades de café conilon (Coffea canephora) para Região Sul do Estado do Espírito Santo.$h[electronic resource]
260 $aIN: SIMPÓSIO DE PESQUISA DOS CAFÉS DO BRASIL, 1., 2000, Poços de Caldas. Resumos Expandidos. Brasília, DF: Embrapa Café/MINASPLAN$c2000
520 $aOs trabalhos de melhoramento de café Conilon da EMCAPER, objetivando disponibilizar variedades melhoradas para os produtores capixabas, abrangeram inicialmente o Norte do Estado do Espírito Santo. Considerando que a área de plantio vem sendo ampliada no Sul, os experimentos de competição passaram a ser conduzidos também nessa região. O Ensaio Regional de Café Conilon foi instalado em maio de 1997, na Fazenda Experimental de Bananal do Norte - FEBN, da EMCAPER, no município de Cachoeiro de Itapemirim, em blocos casualizados, 4 repetições e 8 tratamentos, constituídos de uma variedade de semente, três variedades clonais lançadas pela Ex-EMCAPA, três variedades clonais experimentais e, um Clone Elite, como testemunha. O rendimento médio do experimento variou entre 86 a 118 sc/ben/ha, correspondendo a variedade de semente (EMCAPA 8150) e ao Clone Elite (testemunha), respectivamente. A variedade experimental EMCAPA 8122, de ciclo intermediário, mostrou-se promissora para a região, com rendimento médio de 112 sc/ben/ha.
650 $aClone
650 $aMelhoramento Vegetal
650 $aSeleção
653 $aCultivar
700 1 $aFERRÃO, R. G.
700 1 $aFERRAO, M. A. G.
700 1 $aFONSECA, A. F. A. da
700 1 $aBRAGANÇA, S. M.
700 1 $aARLEU, R. J.
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Registro original: |
Embrapa Café (CNPCa) |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
22/12/2015 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
NESHICH, I. A. P.; NISHIMURA, L.; MORAES, F. R. de; SALIM, J. A.; VILLALTA-ROMERO, F.; BORRO, L.; YANO, I. H.; MAZONI, I.; TASIC, L.; JARDINE, J. G.; NESHICH, G. |
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Afiliação: |
IZABELLA AGOSTINHO PENA NESHICH, Unicamp; LETICIA NISHIMURA, IQSC-USP; FABIO ROGÉRIO DE MORAES, Unesp, Sao José do Rio Preto; JOSE AUGUSTO SALIM, Unicamp; FABIAN VILLALTA-ROMERO; LUIZ BORRO, Unicamp; INACIO HENRIQUE YANO, CNPTIA; IVAN MAZONI, CNPTIA; LJUBICA TASIC, IQ/Unicamp; JOSÉ GILBERTO JARDINE, CNPTIA; GORAN NESHICH, CNPTIA. |
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Título: |
Computational Biology tools for identifying specific ligand binding residues for novel agrochemical and drug design. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Current Protein and Peptide Science, v. 16, n. 8, p. 701-717, 2015. |
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ISBN: |
10.2174/1389203716666150505234923 |
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Idioma: |
Inglês |
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Conteúdo: |
The term ?agrochemicals? is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A ?drug? on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of understanding nanoenvironmental factors that guide recognition and interaction of target protein and its function modifier, an agrochemical or a drug. The repertoire of nanoenvironment descriptors is used for two selected and specific cases we have approached in order to offer a technological solution for some very important problems that needs special attention in agriculture: elimination of pathogenicity of a bacterium which is attacking citrus plants and formulation of a new fungicide. Finally, we also briefly describe a workflow which might be useful when research requires that model structures of target proteins are firstly generated (starting from genome sequences), followed by identification of ligand-target sites at the surface of those modeled structures, then application of procedures that adequately prepare both protein and ligand structures (the latter also involving filtration that satisfies acceptable adsorption/desorption/metabolism/excretion/toxicity [ADMET] parameters) for virtual high throughput screening (involving docking of ligands to indicated sites) and terminating by ranking of best pairs: target protein with selected ligand. MenosThe term ?agrochemicals? is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A ?drug? on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of u... Mostrar Tudo |
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Palavras-Chave: |
Agroquímicos; Bioinformática; Biology; Interaction nanoenvironment; Ligand docking; Nanoambiente; Protein-ligand interactions; Sting structure-function descriptors. |
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Thesagro: |
Biologia; Proteína. |
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Thesaurus NAL: |
Agrochemicals; Bioinformatics; Proteins. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03817naa a2200397 a 4500
001 2032213
005 2020-01-07
008 2015 bl uuuu u00u1 u #d
100 1 $aNESHICH, I. A. P.
245 $aComputational Biology tools for identifying specific ligand binding residues for novel agrochemical and drug design.$h[electronic resource]
260 $c2015
520 $aThe term ?agrochemicals? is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A ?drug? on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of understanding nanoenvironmental factors that guide recognition and interaction of target protein and its function modifier, an agrochemical or a drug. The repertoire of nanoenvironment descriptors is used for two selected and specific cases we have approached in order to offer a technological solution for some very important problems that needs special attention in agriculture: elimination of pathogenicity of a bacterium which is attacking citrus plants and formulation of a new fungicide. Finally, we also briefly describe a workflow which might be useful when research requires that model structures of target proteins are firstly generated (starting from genome sequences), followed by identification of ligand-target sites at the surface of those modeled structures, then application of procedures that adequately prepare both protein and ligand structures (the latter also involving filtration that satisfies acceptable adsorption/desorption/metabolism/excretion/toxicity [ADMET] parameters) for virtual high throughput screening (involving docking of ligands to indicated sites) and terminating by ranking of best pairs: target protein with selected ligand.
650 $aAgrochemicals
650 $aBioinformatics
650 $aProteins
650 $aBiologia
650 $aProteína
653 $aAgroquímicos
653 $aBioinformática
653 $aBiology
653 $aInteraction nanoenvironment
653 $aLigand docking
653 $aNanoambiente
653 $aProtein-ligand interactions
653 $aSting structure-function descriptors
700 1 $aNISHIMURA, L.
700 1 $aMORAES, F. R. de
700 1 $aSALIM, J. A.
700 1 $aVILLALTA-ROMERO, F.
700 1 $aBORRO, L.
700 1 $aYANO, I. H.
700 1 $aMAZONI, I.
700 1 $aTASIC, L.
700 1 $aJARDINE, J. G.
700 1 $aNESHICH, G.
773 $tCurrent Protein and Peptide Science$gv. 16, n. 8, p. 701-717, 2015.
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Embrapa Agricultura Digital (CNPTIA) |
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